| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.421 | | leniency | 0.842 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1187 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 49.45% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1187 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "measured" | | 1 | "glint" | | 2 | "maw" | | 3 | "streaming" | | 4 | "chill" | | 5 | "flicked" | | 6 | "echoed" | | 7 | "velvet" | | 8 | "gleaming" | | 9 | "pulse" | | 10 | "pulsed" | | 11 | "weight" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 122 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 122 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 135 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 29 | | markdownWords | 57 | | totalWords | 1167 | | ratio | 0.049 | | matches | | 0 | "Three blocks. Lost him at the corner of Wardour." | | 1 | "There." | | 2 | "aware" | | 3 | "The Raven’s Nest" | | 4 | "Silas’ bar." | | 5 | "Hostiles?" | | 6 | "detective." | | 7 | "exactly" | | 8 | "Not from anything human." | | 9 | "The Raven’s Nest" | | 10 | "Older than blood." | | 11 | "Again." | | 12 | "Dracula" | | 13 | "no." | | 14 | "wrong." | | 15 | "Sigma for S. Darkness backwards." | | 16 | "Mostly" | | 17 | "Entry fee." | | 18 | "teeth." | | 19 | "They know you don’t belong." | | 20 | "idiot," | | 21 | "people." | | 22 | "moved." | | 23 | "Now." | | 24 | "smooth," | | 25 | "feeling," | | 26 | "Detective." | | 27 | "autopsy." | | 28 | "For her." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 1075 | | uniqueNames | 16 | | maxNameDensity | 0.74 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 8 | | Soho | 1 | | Raven | 2 | | Nest | 3 | | Morris | 1 | | Thames | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 5 | | Silas | 1 | | Greek | 1 | | Bristol | 1 | | Veil | 1 | | Market | 1 | | Tiny | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Morris" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Tomás" | | 8 | "Silas" | | 9 | "Greek" | | 10 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Thames" | | 2 | "Bristol" |
| | globalScore | 1 | | windowScore | 1 | |
| 73.08% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 2 | | matches | | 0 | "quite classify—turned toward her" | | 1 | "looked like eyeballs in brine" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1167 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 135 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 21.22 | | std | 20.86 | | cv | 0.983 | | sampleLengths | | 0 | 75 | | 1 | 59 | | 2 | 42 | | 3 | 46 | | 4 | 2 | | 5 | 68 | | 6 | 6 | | 7 | 46 | | 8 | 9 | | 9 | 18 | | 10 | 46 | | 11 | 7 | | 12 | 21 | | 13 | 44 | | 14 | 46 | | 15 | 20 | | 16 | 27 | | 17 | 9 | | 18 | 3 | | 19 | 54 | | 20 | 11 | | 21 | 5 | | 22 | 60 | | 23 | 37 | | 24 | 35 | | 25 | 4 | | 26 | 3 | | 27 | 5 | | 28 | 64 | | 29 | 31 | | 30 | 5 | | 31 | 44 | | 32 | 8 | | 33 | 8 | | 34 | 15 | | 35 | 8 | | 36 | 19 | | 37 | 10 | | 38 | 16 | | 39 | 5 | | 40 | 50 | | 41 | 1 | | 42 | 5 | | 43 | 7 | | 44 | 5 | | 45 | 5 | | 46 | 3 | | 47 | 19 | | 48 | 3 | | 49 | 3 |
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| 99.51% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 122 | | matches | | |
| 79.52% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 166 | | matches | | 0 | "was walking" | | 1 | "was standing was holding" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 18 | | semicolonCount | 0 | | flaggedSentences | 13 | | totalSentences | 135 | | ratio | 0.096 | | matches | | 0 | "Rain lashed sideways, turning the neon glow of Soho into a blur of smeared colours—red, blue, amber—all bleeding together like a watercolour left in the storm." | | 1 | "The suspect—tall, lean, moving with that unnatural grace that set her teeth on edge—glanced back." | | 2 | "Quinn’s fingers twitched toward her sidearm, but the crowd—drunk, oblivious, stumbling between pubs—was too thick." | | 3 | "Just turned, sprinted, and was gone—swallowed by the dark maw of an alley she knew too well." | | 4 | "She’d seen him at crime scenes before—always on the edges, always too helpful, always with that scar on his forearm peeking out from under his sleeve." | | 5 | "A crash echoed from the alley—metal on stone." | | 6 | "All eyes—human, otherwise, and the ones she couldn’t quite classify—turned toward her." | | 7 | "But the bookshelf at the far end of the room—the one with the first-edition *Dracula* and the taxidermied raven—stood ajar." | | 8 | "The air grew thicker with each step, the damp smell of earth and something older, something *wrong.* Her torch beam cut through the black, illuminating graffiti on the walls—symbols, not tags." | | 9 | "She’d heard the whispers—cops who’d seen things, drunk off-duty, who woke up the next day with their memories fuzzy and their pockets full of bone tokens." | | 10 | "The stalls sold things that didn’t have names—jars of liquid that pulsed like hearts, knives that whispered when you got too close, a cage containing something that might’ve been a child once, before it grew too many limbs." | | 11 | "Then he raised a hand—and the market *moved.*" | | 12 | "It had the shape of a man, but its skin was the wrong colour—too pale, too *smooth,* like polished bone." |
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| 81.10% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 274 | | adjectiveStacks | 1 | | stackExamples | | 0 | "suspect—tall, lean, moving" |
| | adverbCount | 15 | | adverbRatio | 0.05474452554744526 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0072992700729927005 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 135 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 135 | | mean | 8.64 | | std | 7.12 | | cv | 0.824 | | sampleLengths | | 0 | 23 | | 1 | 26 | | 2 | 15 | | 3 | 2 | | 4 | 8 | | 5 | 1 | | 6 | 15 | | 7 | 15 | | 8 | 4 | | 9 | 20 | | 10 | 2 | | 11 | 3 | | 12 | 15 | | 13 | 6 | | 14 | 1 | | 15 | 3 | | 16 | 17 | | 17 | 25 | | 18 | 4 | | 19 | 17 | | 20 | 2 | | 21 | 21 | | 22 | 1 | | 23 | 5 | | 24 | 1 | | 25 | 1 | | 26 | 19 | | 27 | 20 | | 28 | 6 | | 29 | 14 | | 30 | 13 | | 31 | 10 | | 32 | 9 | | 33 | 5 | | 34 | 4 | | 35 | 9 | | 36 | 9 | | 37 | 9 | | 38 | 26 | | 39 | 7 | | 40 | 4 | | 41 | 7 | | 42 | 13 | | 43 | 8 | | 44 | 8 | | 45 | 3 | | 46 | 17 | | 47 | 16 | | 48 | 11 | | 49 | 8 |
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| 61.19% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.40298507462686567 | | totalSentences | 134 | | uniqueOpeners | 54 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 103 | | matches | | 0 | "Just for a second." | | 1 | "Just turned, sprinted, and was" | | 2 | "*Mostly* not human." | | 3 | "Then he raised a hand—and" | | 4 | "Then the lights went out." |
| | ratio | 0.049 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 103 | | matches | | 0 | "Her breath came in controlled" | | 1 | "She skidded to a halt" | | 2 | "His Saint Christopher medallion glinted," | | 3 | "She knocked his hand off." | | 4 | "His gaze flicked past her," | | 5 | "She’d seen him at crime" | | 6 | "She pushed past him, boots" | | 7 | "His smile didn’t reach his" | | 8 | "She ignored him." | | 9 | "She didn’t listen." | | 10 | "She’d heard the whispers—cops who’d" | | 11 | "He stood at a stall" | | 12 | "She could turn back." | | 13 | "She stepped forward." | | 14 | "It was *teeth.* Tiny, sharp," | | 15 | "Her suspect was moving again," | | 16 | "She kept her hand near" | | 17 | "*They know you don’t belong.*" | | 18 | "His face was tight, his" | | 19 | "His grip tightened" |
| | ratio | 0.243 | |
| 91.07% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 103 | | matches | | 0 | "The pavement exploded beneath Quinn’s" | | 1 | "Rain lashed sideways, turning the" | | 2 | "Her breath came in controlled" | | 3 | "A shadow detached itself from" | | 4 | "The suspect—tall, lean, moving with" | | 5 | "Quinn’s fingers twitched toward her" | | 6 | "A shot here would bring" | | 7 | "A suspension, maybe." | | 8 | "The words tore from her," | | 9 | "The suspect didn’t flinch." | | 10 | "She skidded to a halt" | | 11 | "The Nest would be packed." | | 12 | "*Hostiles?* The last time she’d" | | 13 | "A hand clamped onto her" | | 14 | "Quinn spun, fist already bunched," | | 15 | "Tomás Herrera’s voice cut through" | | 16 | "His Saint Christopher medallion glinted," | | 17 | "She knocked his hand off." | | 18 | "His gaze flicked past her," | | 19 | "The air between them hummed," |
| | ratio | 0.738 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 103 | | matches | | 0 | "Because the suspect was walking" | | 1 | "Because she’d seen it before." | | 2 | "To the suspect." |
| | ratio | 0.029 | |
| 55.75% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 5 | | matches | | 0 | "The suspect—tall, lean, moving with that unnatural grace that set her teeth on edge—glanced back." | | 1 | "She pushed past him, boots splashing through puddles that reflected the sickly green of the bar’s sign." | | 2 | "She’d heard the whispers—cops who’d seen things, drunk off-duty, who woke up the next day with their memories fuzzy and their pockets full of bone tokens." | | 3 | "The stalls sold things that didn’t have names—jars of liquid that pulsed like hearts, knives that whispered when you got too close, a cage containing something …" | | 4 | "It was a *feeling,* crawling up her spine, settling in her skull." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 44.74% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "he hissed (hiss)" | | 1 | "she snapped (snap)" |
| | dialogueSentences | 19 | | tagDensity | 0.211 | | leniency | 0.421 | | rawRatio | 0.5 | | effectiveRatio | 0.211 | |